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Glama

Mfapi India

by io.github.pipeworx-io

Search Schemes

search_schemes
Read-onlyIdempotent

Find Indian mutual-fund scheme codes by fund name. Fetches the full MFAPI scheme list (~16,000 schemes) and filters client-side by case-insensitive token match (every word in the query must appear in the scheme name, any order — so "SBI Bluechip" matches "SBI Blue Chip Fund"). This is the way to obtain a scheme_code for get_nav_history and latest_nav. Note: this call downloads the full list (~1-2MB). Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax schemes to return (default 15, max 30).
queryYesFund-name fragment, e.g. "SBI Bluechip", "Parag Parikh", "index fund". Matched case-insensitively against scheme names.

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false), the description adds crucial details: it downloads the full ~16,000 scheme list (~1-2MB), filters client-side with case-insensitive token matching, and notes it is 'Keyless'. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences front-loaded with the main purpose. Every sentence adds essential information: what it does, how it works, and its role in the tool ecosystem. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and no output schema, the description covers all necessary context: purpose, behavior (client-side filtering, download size), parameter semantics, and its relationship to sibling tools. The output (scheme_code) is explicitly mentioned.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description adds value by explaining the matching logic for 'query' (every word must match in any order) and notes the default limit. This goes beyond the schema, which only provides basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Find Indian mutual-fund scheme codes by fund name' with a specific verb and resource. It distinguishes this tool from siblings like get_nav_history and latest_nav by explaining it obtains the scheme_code needed for those tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: this tool is for obtaining scheme_codes for get_nav_history and latest_nav. It also mentions the download size and matching logic. However, it does not explicitly state when not to use this tool or provide alternatives for other lookup purposes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose. The various `ask_pipeworx*` variants are differentiated by mode (single vs grounded vs research). `get_nav_history` vs `latest_nav` serve different query granularities. Administrative tools like `remember`/`recall`/`forget` are clearly separate. No two tools overlap in functionality.

Naming Consistency4/5

Tool names follow a consistent snake_case convention and generally use `verb_noun` order (e.g., `ask_pipeworx`, `search_schemes`, `validate_claim`). A few exceptions like `entity_profile` (noun_verb) exist, but the pattern is mostly predictable.

Tool Count3/5

At 34 tools, the server is quite large, including many specialized tools (e.g., multiple Polymarket tools, administrative memory/subscription tools) that could arguably be split into separate servers. The number feels slightly excessive for a coherent, focused server.

Completeness5/5

The tool set covers an extraordinarily wide range of domains: company financials, SEC filings, FDA drugs, economic data, mutual funds, real estate, prediction markets, npm dependencies, AI visibility, and more. It also includes memory, subscription, and feedback mechanisms. There are no obvious gaps for the domains addressed.

Resources